Your custom metrics

Defining your own metric is very easy. You can create both table-level or column-level metrics.

Metrics can be defined in any place in your dbt project, as macros with names following the pattern: re_data_metric_(your_name)

• Both column and table level metrics take a dictionary called context. Any extra configuration passed to a re_data_metrics config would be merged with the context dicionary and avaiable under config key. Check out example usage config dict in match_regex metric.

# Below is the structure of a context dictionary by default
{
    "column_name": # contains the name of the column to compute the metric on. null in table metrics
    "table_name": # contains the full table name for reference in metric query definition
    "metric_name": # name of the metric being computed
    "time_filter": # time column used to filter the time window
    "config": # none or dictionary containing arguments passed to custom metrics
}

Here are examples of custom metrics (one table, one column level)

macros/my_metrics.sql

{% macro re_data_metric_buy_count(context) %}
    coalesce(
        sum(
            case when event_type = 'buy'
                then 1
            else 0
            end
        ), 0
    )
{% endmacro %}

{% macro re_data_metric_distinct_count(context) %}
    count(distinct( {{context.column_name}} ))
{% endmacro %}

{# a more sophisticated example of custom metric with subquery #}
{% macro re_data_metric_duplicate_values(context) %}
        with temp_table as (
            select {{ context.column_name }} from {{ context.table_name }}
            where {{ in_time_window(context.time_filter) }}
            group by {{ context.column_name }}
            having count(1) > 1
        )
        select coalesce(count(*), 0) from temp_table
{% endmacro %}

Some important difference between table and column level metrics are:

• Table level metrics reference column names directly.
• Column level metrics reference it through context.column_name variable. (Which makes them more re-usable)
• Tip: When using sub queries in custom metrics, remember to filter the records to the time frame in context. We can use in_time_window({{context.time_fiter}}) macro to achieve this. The macro distinct_table_rows is a good example.

Your metric ideas

If you have other suggestions of metrics you would like supported, please let us know on Slack! 😊